Update README.md

README.md

@@ -1,3 +1,47 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: text-classification
+tags:
+- sentiment-analysis
+- text-classification
+- generic
+- sentiment-classification
+- multilingual
 ---
+
+## Model
+
+Base version of e5-multilingual finetunned on an annotated subset of mC4 (multilingual C4). This model provide generic embedding for sentiment analysis. Embeddings can be used out of the box or fine tune on specific datasets. 
+
+
+## Usage
+
+Below is an example to encode text and get embedding.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+
+model = AutoModel.from_pretrained("Numind/e5-multilingual-sentiment_analysis")
+tokenizer = AutoTokenizer.from_pretrained("Numind/e5-multilingual-sentiment_analysis")
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+model.to(device)
+
+size = 256
+text = "This movie is amazing"
+
+encoding = tokenizer(
+    text,
+    truncation=True, 
+    padding='max_length', 
+    max_length= size,
+)
+
+emb = model(
+      torch.reshape(torch.tensor(encoding.input_ids),(1,len(encoding.input_ids))).to(device),output_hidden_states=True
+).hidden_states[-1].cpu().detach()
+
+embText = torch.mean(emb,axis = 1)